Non-HDL-C and LDL-C/HDL-C are associated with self-reported cardiovascular disease in a rural West African population: Analysis of an array of lipid metrics in an AWI-Gen sub-study

Few studies have compared the utility of serum levels of lipid fractions in cardiovascular disease (CVD) risk assessment in sub-Saharan Africa (SSA). The current study interrogated this question among men and women aged 40–60 years in rural northern Ghana. This was a cross-sectional study in which data was collected on socio-demography, behaviour, health history, anthropometry and lipid levels. Adjusted multivariable logistic regression models were used to assess the association of various lipid metrics with CVD. All tests were considered statistically significant at P<0.05. Data were available for 1839 participants. The prevalence of self-reported CVD was 1.6% (n = 29). Non-HDL-C (median (interquartile range): 2.4 (1.9–3.0) vs 2.0 (1.6–2.5) mmol/L; P = 0.009), LDL-C/HDL-C (1.8 (1.4–2.4) vs 1.5 (1.1–2.6); P = 0.019) and TC/HDL-C (3.3 (2.9–3.9) vs 2.9 (2.4–3.5); P = 0.003) were all significantly higher in participants with self-reported CVD compared to those without. However, after adjusting for socioeconomic status (SES) and meals from vendors in a logistic regression model, only non-HDL-C (odds ratio [95% CIs]): (1.58 [1.05, 2.39]), P = 0.029 and LDL-C/HDL-C levels (odds ratio [95% CIs]): (1.26 [1.00, 1.59]), P = 0.045 remained significantly associated with self-reported CVD. While our findings suggest non-HDL-C and LDL-C/HDL-C measures may be appropriate biomarkers for assessing CVD risk in this population, further studies using established clinical endpoints are required to validate these findings in sub-Saharan Africans.

The current study therefore investigated the association of various lipid metrics with selfreported CVD. The novelty of this study is in the use of a wide array of lipid metrics in the investigation of CVD in a large SSA population in which data is available on CVD endpoints. Particularly, this is the first study to carry out such analyses in a rural, adult population resident in Ghana which is known to have a high prevalence of low HDL-C [26]. In addition, a recent meta-analysis has demonstrated a high prevalence of dyslipidaemia across the African continent [28] and this highlights the importance of determining the association of lipid levels with CVD in African populations.

Study design
This was a population based cross-sectional study that was conducted as part of the Africa Wits-INDEPTH Partnership for Genomic studies (AWI-Gen) project from 2013 to 2017 under the broader Human Heredity and Health in Africa (H3Africa) initiative [29]. The study was conducted in accordance with the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guidelines for the presentation of reports of cross sectional studies [30].

Study setting
The study was conducted in the two Kassena-Nankana districts (KNDs) (Kassena-Nankana east and west) of northern Ghana that share a border with southern Burkina Faso. The two districts are covered by the Navrongo Health and Demographic Surveillance System (NHDSS) which has categorized the area into five zones according to the geographical cardinal points (east, west, north, south and central zone). Each zone is further divided into clusters. The study setting is mainly rural with mostly agricultural activities and covers a total land area of approximately 1675 km 2 and with an estimated population size of 167500 people [31].

Study population
The study population consisted of men and women aged 40-60 years who were resident within the community for at least ten years and agreed by written informed consent to participate in the study. Pregnant women and individuals who could not stand upright were excluded because their weight and standing height could not be accurately measured. Participants were selected using stratified random sampling from the two KNDs of northern Ghana. Four zones (east, west, north and south) of the KNDs were first selected and from each of these zones twenty five clusters were randomly sampled using the Navrongo Health and Demographic Surveillance System (NHDSS) [31]. A list of 2200 men and women including 10% for nonresponse or refusal was generated from the sampled clusters. The sample size in each cluster was proportional to its population. Individuals who agreed to participate in the study provided informed consent and were assigned unique identification numbers to ensure anonymity [26,29]. A sample size of 2019 was recruited into the study but a complete case analysis was conducted using an analytical dataset of 1839.

Sample size determination
This study was 99.99% powered to detect dyslipidaemia with a minimum sample size of 1441 at an acceptable margin of 5% assuming a population size of about 165000 in the KNDs [31] and considering the prevalence of dyslipidaemia, as defined by low HDL-C, as 60.30% [26]. The sample size was determined using epi info TM software version 7.2.2. 16 [32].

Data collection
Potential participants in the selected sample were invited to a common venue for recruitment. Following community engagement and informed consent, data including age, sex, diet (vegetable and fruit intake, and vendor meals consumed), medication use, physical activity, smoking, socio-economic status (SES), previous congestive heart failure, myocardial infarction and stroke were collected using an interview-administered structured questionnaire. Standing height of participants was measured using a Harpenden stadiometer (Holtain, Crymych Wales) fixed to a wall while weight was measured using a weighing scale (Kendon Medical, South Africa). Waist and hip circumference of participants in light clothes were measured using a stretch-resistant tape measure (SECA, Hamburg, Germany). Blood pressure was measured using a digital sphygmomanometer (Omron M6, Omron, Kyoto, Japan) with the measurement taken thrice at two-minute intervals between each measure. The systolic blood pressure (SBP) and the diastolic blood pressure (DBP) were calculated using the means of the last two measurements [33]. Visceral adipose tissue thickness (VAT) and subcutaneous adipose tissue thickness (SAT) were measured twice using a LOGIQ e ultrasound system (GE, Healthcare, CT, USA) and their mean values calculated. All data was entered into a paper response form and captured into the REDCap platform [34]. As part of the quality control process 10% of all data entries were checked for data entry consistency and all missing variables were noted [26]. Details of data collection for age, sex, physical activity, diet, cigarette smoking and anthropometric measures (weight, height, waist and hip circumference) were previously described [26].

Biomarker analysis
Fasting blood glucose, LDL-C, HDL-C, TC and TG were all measured directly using an automated chemistry analyzer (Randox RX Daytona+, Crumlin, Northern Ireland) as described elsewhere [26,29]. Non-HDL-C was calculated by subtracting HDL-C from TC [9]. The fasting proatherogenic lipid ratios, i.e. TG/LDL-C, TC/HDL-C and LDL-C/HDL-C, were calculated. Remnant cholesterol was calculated by subtracting the sum of LDL-C and HDL-C from TC (TC-[LDL-C + HDL-C]) [15,33]. To ensure quality control a random selection of 150 samples were analyzed in duplicate for glucose and the lipid fractions to ascertain the coefficient of variation (CV) of the assay.

Ethics approval and consent to participate
This was a sub-study of the AWI-Gen (Africa Wits-INDEPTH Partnership for Genomic Research) project that was approved by the Human Research Ethics Committee (HREC) of the University of the Witwatersrand (ID No: M12109), the Ghana Health Service Ethics Review Committee (ID No: GHS-ERC:05/05/2015) and the Navrongo Institutional Review Board (ID No: NHRCIRB178). The study was conducted in accordance with United States federal code of ethics. Community engagement was carried out in the communities where participants were sampled. Individual broad informed consent, evidenced by a thumb-printed or signed informed written consent form witnessed by a researcher, was sought from participants before being recruited into the study.

Definitions
Congestive heart failure, myocardial infarction (MI) and stroke were self-reported incidents prior to the time of recruitment and CVD was then defined as a self-reported incident of congestive heart failure, stroke or MI. Diet was determined by the average number of self-reported servings of food prepared by street vendors (the main commercial source of prepared food in this geographical area) per month, average number of self-reported consumption of fruits or vegetables per week. Smoking was defined as being a current smoker or non-smoker. High SES was defined as the fourth and fifth quintiles that were derived from the scores developed from principal components computed using household assets (http://indepth-network. org/resources/indepth-health-equity-tool-measuringsocio-economic-status). Physical activity was determined using the Global Physical Activity Questionnaire (GPAQ) [35]. Moderate to vigorous-intensity physical activity (MVPA) was defined as minutes of physical activity per week (min/week). Low physical activity was defined as MVPA<150min/week while normal/ high physical activity defined as MVPA�150min/week. Body mass index (BMI) was computed as weight (kg)/height 2 (m 2 ) with obesity as BMI�30kg/m 2 [36]. Hypertension was defined as SBP>140mmHg and/or DBP>90mmHg or self-reported treatment using anti-hypertensive medication [37]. High non-HDL-C was defined as non-HDL-C>3.4mmol/l, low HDL-C level as <1.0mmol/l for men and <1.2mmol/l for women, high LDL-C level as >3.0mmol/l and high TG was defined as >1.

Statistical analyses
Data analyses were performed using STATA version 14.2 (StataCorp, College Station, Texas, US). Continuous variables were skewed and were presented as medians with interquartile ranges (IQR) and compared between individuals with and without self-reported CVD using the Mann Whitney U test while categorical variables were compared between those with and without self-reported CVD using Pearson's χ 2 test. Multivariable logistic regression models were used to assess the association of each of the lipid metrics expressed as continuous variables with self-reported CVD. Variables that were significantly different between individuals with and without self-reported CVD were considered confounders and were adjusted for in each of the multivariable logistic regression models. Variables were considered collinear if they had variance inflation factor (VIF)>5.0, but no such events were observed. All tests were considered statistically significant at P<0.05.

Characteristics of the study participants
The study participants were categorized under those with and those without self-reported CVD as illustrated in Table 1. The study population was made up of 46% men and 54% women. The proportion of current smokers in the population was 10.82% while that of physically active and obese individuals was 85.75% and 2.66% respectively. The burden of hypertension was 21.70%. The proportion of high SES individuals was greater among those with selfreported CVD than those without self-reported CVD (P = 0.013). Similarly, the average number of meal servings from vendors per month was higher among those with self-reported CVD than those without self-reported CVD (P = 0.013).
The prevalence of self-reported CVD in the total population was only 1.58% whilst that for each of self-reported stroke, heart disease or myocardial infarction was less than 1.0% (Table 2). In participants with self-reported CVD, the most common self-reported CVD endpoint was myocardial infarction (51.7%) followed by stroke (48.3%) and congestive heart failure (3.45%). Only two participants self-reported receiving anti-lipid therapy (Table 1). After running the regression analysis with and without these participants the results remained unchanged. Therefore, these participants were not excluded from the analysis. Median non-HDL-C (P = 0.009), TC/HDL-C (P = 0.003) and LDL-C/HDL-C levels (P = 0.019) of participants with self-reported CVD were significantly higher than the respective levels of those without self-reported CVD (Table 2).
Similarly, dyslipidaemia in individuals with and without self-reported CVD were compared. The prevalence of high non-HDL-C was significantly (13.8%) higher among individuals with self-reported CVD than individuals without self-reported CVD (6.1%) (P<0.050) (Fig 1).  Table 3 shows unadjusted and adjusted odds ratios (with 95% CIs) for self-reported CVD for each of the nine lipid metrics in nine separate regression models that were unadjusted or adjusted for SES and meals purchased from vendors. Adjustments were made for these variables because they were the only variables that were found to be significantly different between subjects with and without CVD (see Table 1). In the unadjusted models only non-HDL-C (Table 3, model 5) and LDL-C/HDL-C (Table 3, model 7) were significantly (P = 0.01 and P = 0.02, respectively) associated with CVD. After adjusting for household SES and consumption of vendor meals participants with high non-HDL-C were 58% more likely to develop CVD [(1.58 (1.05, 2.39), P = 0.029] and those with high LDL-C/HDL-C levels were 26% more likely to develop CVD [(1.26 (1.00, 1.59), P = 0.045] compared to subjects with normal levels of these lipid metrics. Consumption of vendor meals was significantly associated with selfreported CVD in all nine regression models whilst SES tended toward a significant association in some of the models with P values ranging from 0.051 to 0.155 (see Table 3).

Discussion
This study of rural Ghanaian adult men and women investigated the association of a wide array of lipid metrics with self-reported CVD. The results showed that only 1.58% of the population reported a cardiovascular event. The lipid metrics associated with self-reported CVD were non-HDL-C and LDL-C/HDL-C. The prevalence (1.58%) of self-reported CVD in this rural population was lower than that reported (8.2%) earlier in hospital admissions in urban Ghana [42] and in other studies in SSA [23]. The prevalence of self-reported CVD in this study may be under reported and further studies involving confirmed clinical cases are required to support these findings. However, the low prevalence of both obesity and current smoking and the high level of physical activity  reported in this population may partly explain the low level of self-reported CVD events, despite the high prevalence of hypertension. High physical activity, non-smoking and lack of obesity are reported to be associated with healthy cardiovascular outcomes [43]. The strong association of non-HDL-C with self-reported CVD reinforces the recommendation in the latest guidelines for both European [44] and American Cardiology Societies [45] for inclusion of non-HDL-C in the assessment of CVD risk. The observed stronger link of non-HDL-C than several lipid metrics in this study with CVD risk could be due to the representation of all atherogenic apolipoprotein B (ApoB) containing lipoproteins in the non-HDL-C level [10]. Previous findings by Liu et al reported that increased levels of non-HDL-C increased the risk of death among diabetics with acute coronary heart disease and myocardial infarction to a greater extent than did LDL-C, which is considered a primary CVD marker [20]. Other studies in Africans [46] and non-Africans [47,48] have reported the usefulness of non-HDL-C in CVD risk assessment and as a prognostic marker in CVD treatment. In spite of the low levels of HDL-C reported earlier in this population [26], HDL-C was not associated with selfreported CVD in this study. This is consistent with earlier reports which showed that isolated low HDL-C levels may not necessarily reflect CVD risk [49]. Rather, HDL-C sub-fractions have been reported to improve CVD prediction [50,51] Future studies are therefore recommended to investigate the relationship of HDL-C sub-fractions with CVD risk in this population. The observed association of LDL-C/HDL-C ratio with self-reported CVD in all the models is supported by earlier reports of a relationship of this lipid ratio with sudden cardiac death [52] and CVD risk in other populations [53]. It is interesting to note that although high prevalence of low HDL-C was reported in this population [26] HDL-C was not associated with selfreported CVD neither was LDL-C. These data suggest that these individual lipids are not strong markers of CVD but when used in combination become more strongly associated with self-reported CVD. The association of LDL-C/HDL-C with CVD could be attributed to the atherogenic potential of the numerator and the anti-atherosclerotic potential of the denominator acting in combination to provide a stronger association with CVD than that of each lipid species alone [54,55].
The novelty of this study is that it is the only analysis conducted in SSA of the association of commonly-used lipid metrics with CVD endpoints. This is important because it is known that CVD prevalence is increasing in this region [56,57] and globally, 80% of deaths related to CVD occur in low-and middle-income countries [2]. In addition, the INTERHEART study demonstrated that premature acute myocardial infarction occurred more often in countries within SSA than in any of the other 52 countries included in that study [58]. It is therefore important to understand the risk factors associated with CVD in SSA and to develop cheap and effective methods for screening for subjects at high risk for these diseases. Dyslipidaemia is prevalent in SSA [28] and is a well-recognized risk factor for CVD and therefore was the focus of this study. The strength of this study is that it is the first to evaluate and compare the association of a wide array of lipid metrics with self-reported CVD endpoints in a large adult SSA population.
Unlike several studies that derived LDL-C from the Friedewald equation, which has several limitations [59], a direct measurement method was used and stringent quality control performed in this study. However, the study was not without limitations. First, it was a cross-sectional analysis of self-reported CVD and clinical data was not used to capture the occurrence of CVD events. Therefore, causal relationships could not be proven. Furthermore, only a small number of participants with self-reported CVD were identified in the study and this reduces the power of the analysis. There is also a shortcoming in the lack of data on apolipoproteins [60] and HDL-C subfractions [50,51] which are known to be more reliable indicators of CVD risk. Current smoking, physical activity, SES, CVD and diet variables were self-reported and could be prone to bias.

Conclusion
This is the first study in SSA to assess the association of a wide array of lipid metrics with selfreported CVD. The results suggest a low CVD burden and that non-HDL-C and LDL/HDL-C were associated with CVD and could be used as indicators of CVD risk and prognostic markers of therapy in this study population. Future studies involving longitudinal cohorts and incident cardiovascular cases are recommended to confirm the association of these lipid metrics with CVD.  48. Sone H, Nakagami T, Nishimura R, Tajima N. Comparison of lipid parameters to predict cardiovascular events in Japanese mild-to-moderate hypercholesterolemic patients with and without type 2 diabetes: